Session info

This file presents the results of Stan model

## [1] "age_hfr_210619b.stan"

with job tag

## [1] "hfr-fit-210619b1-resources-ex0-trvaluelogsc-hd1-htprivate"

Objectives

Several studies have reported significantly higher, crude in-hospital fatality rates following COVID-19 attributable hospital admission across Brazil after P.1 detection. The overall, primary objective of this study was to

More detailed objectives were to

Methods

We

Results

Population denominators

As population denominators, we used 2020 population size projects from the PNADc survey of the Instituto Brasileiro de Geografia e Estatística (IBGE). Population denominators were not consistent with individual-level data on vaccine administrations in each location, which could be due to unreported location of residence at time of vaccine administration. We adjusted population denominators upwards so that at most 99 percent of the population had received a first dose, and never adjusted population denominators downwards.

COVID-19 attributable fatal outcomes

Reported COVID-19 attributable deaths were stratified by location of death (out of hospital, private hospitals, public hospitals, private or public hospitals). Reported deaths in hospitals were adjusted upwards to account for patients with unkwnown outcomes. Censoring-adjusted deaths were compared to excess deaths:

Age composition of COVID-19 attributable fatal outcomes

We find substantial changes in the age composition of COVID-19 attributable deaths over time, since vaccine roll-out and since P.1 emergence:

Cities 1

Cities 2

Cities 3

Vaccine roll-out

In our modelling we adjusted for protection from fatal outcomes 2 weeks after vaccine administration:

Cities 1

Cities 2

Cities 3

Health care demand predictors

ICU beds per 100,000

Physicians per 100,000

Specialist physicians per 100,000 (cardiologists, anesthesiologists, intensive care)

Ventilators per 100,000

Proportion of residents among hospital admissions in this and past four weeks

SARI admissions in this and next four weeks per 100,000

SARI admissions in this and next two weeks per ICU bed

SARI admissions in this and next two weeks per physician

ICU admissions in this and next two weeks per ICU bed

ICU admissions in this and next two weeks per physician

ICU admissions in this and next four weeks per specialist physician

ICU admissions in this and next two weeks per ventilator

Out of hospital deaths in this and next four weeks

P.1 replacement dynamics by city

P.1 genotype frequency

COVID-19 attributable deaths

Association in-hospital fatality rates <-> health care demand predictors

Location effect to in-hospital fatality rates

Ratio in-hospital fatality rates in reference week across locations

In-hospital fatality rates in reference week versus health care demand predictors

Multiplier to in-hospital fatality rates based on health care demand predictors

Scenario compared to reference week, multiplier to in-hospital fatality rates

Scenario compared to reference week, deaths averted

Scenario compared to best city, multiplier to in-hospital fatality rates

Scenario compared to best city, excess deaths

P.1 effect to in-hospital fatality rates

Absolute in-hospital fatality rates by variant

Ratio in-hospital fatality rates P.1 vs non-P.1

Ratio share of age groups P.1 vs non-P.1

Model fits by city

Belo Horizonte

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Curitiba

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Goiania

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Manaus

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Natal

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Rio de Janeiro

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Salvador

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

Sao Paulo

Fit to genotype data

Fit to hospital admission data

Fit to death data

Fit to share of age groups in deaths

In-hospital fatality rate multipliers by city

Belo Horizonte

Curitiba

Goiania

Manaus

Natal

Rio de Janeiro

Salvador

Sao Paulo